Shape memory polymer composites (SMPCs) using interconnected nanowire network foams as reinforcements.

Shape memory polymers (SMPs), although offer a suite of advantages such as ease of processability and lower density, lag behind their shape memory alloy counterparts, in terms of mechanical properties such as recovery stress and cyclability. Reinforcing SMPs with inorganic nanowires and carbon nanotubes (CNTs) is a sought-after pathway for tailoring their mechanical properties. Here, inorganic nanowires also offer the added advantage of covalently binding the fillers to the surrounding polymer matrices via organic molecules. The SMP composites (SMPCs) thus obtained have well-engineered nanowire-polymer interfaces, which could be used to tune their mechanical properties. A well-known method of fabricating SMPCs involving casting dispersions of nanowires (or CNTs) in mixtures of monomers and crosslinkers typically results in marginal improvements in the mechanical properties of the fabricated SMPCs. This is owed to the constraints imposed by the rule-of-mixture principles. To circumvent this limitation, a new method for SMPC fabrication is designed and presented. This involves infiltrating polymers into pre-fabricated nanowire foams. The pre-fabricated foams were fabricated by consolidating measured quantities of nanowires and a sacrificial material, such as (NH4)2CO3, followed by heating the consolidated mixtures for subliming the sacrificial material. Similar to the case of traditional composites, use of silanes to functionalize the nanowire surfaces allowed for the formation of bonds between both the nanowire-nanowire and the nanowire-polymer interfaces. SMPCs fabricated using TiO2nanowires and SMP composed of neopentyl glycol diglycidyl ether and poly(propylene glycol) bis(2-aminopropyl ether) (Jeffamine D230) in a 2:1 molar ratio exhibited a 300% improvement in the elastic modulus relative to that of the SMP. This increase was significantly higher than SMPC made using the traditional fabrication route. Well-known powder metallurgy techniques employed for the fabrication of these SMPCs make this strategy applicable for obtaining other SMPCs of any desired shape and chemical composition.

PIMesh: An automatic point cloud and unstructured mesh generation algorithm for meshless methods and finite element analysis-with applications in surgical simulations.

We propose a point cloud and mesh generation algorithm, particle injection mesh generator (PIMesh), that can be used to generate optimized high-quality point clouds and unstructured meshes for domains in any shape with minimum (or even no) user intervention. The domains can be scanned images in OBJ format in 2D and 3D or just a line drawing in 2D. Mesh grading can also be easily controlled. The PIMesh is robust and easy to be implemented and is useful for a variety of applications, ranging from generating point clouds for meshless methods, mesh generation for finite element methods, computer graphics applications and surgical simulators. The core idea of the PIMesh is that a mesh domain is considered as an "airtight container" into which particles are "injected" at one or multiple selected interior points. The motion of the particles is controlled by a pseudo-molecular dynamics (PMD) formulation with a pairwise purely repelling "force" moderated by an absolute velocity dependent drag force. The particles repel each other and occupy the whole domain somewhat like blowing up a balloon. When the container is full of particles and the motion is stopped (the particles can be considered as a point cloud), a Delaunay triangulation algorithm is employed to link the particles together to generate an unstructured mesh. The performance of the PIMesh and the comparison with other unstructured mesh generation approaches are demonstrated through generating node distributions and meshes for several 2D and 3D object domains including a scanned image of bones and others.

Random Forests Are Able to Identify Differences in Clotting Dynamics from Kinetic Models of Thrombin Generation.

Current methods for distinguishing acute coronary syndromes such as heart attack from stable coronary artery disease, based on the kinetics of thrombin formation, have been limited to evaluating sensitivity of well-established chemical species (e.g., thrombin) using simple quantifiers of their concentration profiles (e.g., maximum level of thrombin concentration, area under the thrombin concentration versus time curve). In order to get an improved classifier, we use a 34-protein factor clotting cascade model and convert the simulation data into a high-dimensional representation (about 19000 features) using a piecewise cubic polynomial fit. Then, we systematically find plausible assays to effectively gauge changes in acute coronary syndrome/coronary artery disease populations by introducing a statistical learning technique called Random Forests. We find that differences associated with acute coronary syndromes emerge in combinations of a handful of features. For instance, concentrations of 3 chemical species, namely, active alpha-thrombin, tissue factor-factor VIIa-factor Xa ternary complex, and intrinsic tenase complex with factor X, at specific time windows, could be used to classify acute coronary syndromes to an accuracy of about 87.2%. Such a combination could be used to efficiently assay the coagulation system.

A model of stress and strain in the interosseous ligament of the forearm based on fiber network theory.

Fiber network theory was developed to describe cloth, a thin material with strength in the fiber directions. The interosseous ligament (IOL) of the forearm is a broad, thin ligament with highly aligned fibers. The objectives of this study were to develop a model of the stress and strain distributions in the IOL, based on fiber network theory, to compare the strains from the model with the experimentally measured strains, and to evaluate the force distribution across the ligament fibers from the model. The geometries of the radius, ulna, and IOL were reconstructed from CT scans. Position and orientation of IOL insertion sites and force in the IOL were measured during a forearm compression experiment in pronation, neutral rotation, and supination. An optical image-based technique was used to directly measure strain in two regions of the IOL in neutral rotation. For the network model, the IOL was represented as a parametric ruled three-dimensional surface, with rulings along local fiber directions. Fiber strains were calculated from the deformation field, and fiber stresses were calculated from the strains using average IOL tensile properties from a previous study. The in situ strain in the IOL was assumed uniform and was calculated so that the net force predicted by the network model in neutral rotation matched the experimental result. The net force in the IOL was comparable to experimental results in supination and pronation. The model predicted higher stress and strain in fibers near the elbow in neutral rotation, and higher stresses in fibers near the wrist in supination. Strains in neutral forearm rotation followed the same trends as those measured experimentally. In this study, a model of stress and strain in the IOL utilizing fiber network theory was successfully implemented. The model illustrates variations in the stress and strain distribution in the IOL. This model can be used to show surgeons how different fibers are taut in different forearm rotation positions-this information is important for understanding the biomechanical role of the IOL and for planning an IOL reconstruction.